https://github.com/jordanconallluthaiswright/purchase-behaviour-data-analysis
This project analyzes Black Friday purchase behavior for Company XYZ, uncovering trends by gender, age, and location. Using data cleaning, statistical analysis, and visualization, it evaluates spending patterns, confidence intervals, and category preferences to provide actionable insights for optimizing marketing strategies and targeting.
https://github.com/jordanconallluthaiswright/purchase-behaviour-data-analysis
business-analytics data-analysis jupyter-notebook python
Last synced: 2 months ago
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This project analyzes Black Friday purchase behavior for Company XYZ, uncovering trends by gender, age, and location. Using data cleaning, statistical analysis, and visualization, it evaluates spending patterns, confidence intervals, and category preferences to provide actionable insights for optimizing marketing strategies and targeting.
- Host: GitHub
- URL: https://github.com/jordanconallluthaiswright/purchase-behaviour-data-analysis
- Owner: JordanConallLuthaisWright
- Created: 2025-02-18T12:53:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-18T13:42:49.000Z (over 1 year ago)
- Last Synced: 2025-02-18T14:21:34.086Z (over 1 year ago)
- Topics: business-analytics, data-analysis, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 19.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Black Friday Purchase Behavior Analysis
## Overview
This project presents an in-depth **business data analysis** on customer purchase behavior during **Black Friday sales** for *Company XYZ*. The objective is to uncover key insights about **spending trends based on gender, age, and other factors** to drive data-driven decision-making.
The analysis is performed using structured datasets and statistical techniques to identify **consumer purchasing patterns** and **recommendations for business optimization**.
## Files in This Repository
- **`XYZ_data.csv`** – The raw dataset containing transaction records.
- **`README.md`** – This document explaining the project and its methodology.
- **`Black Friday Purchase Behavior Analysis.zip`** – The complete project package. Download and unzip to run the analysis.
- **`Black Friday Purchase Behavior Analysis (without any cells running).ipynb`** – A static preview of the Jupyter Notebook for quick reference.
## Business Scenario
Company XYZ aims to understand **consumer spending behavior during Black Friday**, focusing on the following key questions:
1. **Do women spend more than men?**
2. **How do demographics such as age, marital status, and location influence purchases?**
3. **Which product categories generate the highest sales?**
4. **What insights can be derived to improve future marketing strategies?**
## Methodology & Skills Demonstrated
### 1. Data Cleaning & Preprocessing
- Loaded and structured data using **Pandas**.
- Removed duplicates, handled missing values, and formatted data for analysis.
- Converted categorical variables into appropriate formats.
### 2. Exploratory Data Analysis (EDA)
- Analyzed **purchase trends by gender, age, and location**.
- Created **visualizations using Matplotlib & Seaborn** to identify spending patterns.
- Used statistical summaries to examine purchase behaviors.
### 3. Statistical & Hypothesis Testing
- **Confidence Interval Analysis**: Assessed average spending differences between male and female customers.
- **Central Limit Theorem (CLT) & Bootstrapping**: Simulated population spending behaviors.
- **Comparative Analysis**: Studied spending habits across different age groups and marital statuses.
### 4. Insights & Recommendations
- Identified **high-spending demographics** to optimize marketing campaigns.
- Recommended **targeted promotions for different customer segments**.
- Suggested **enhancements in product category distribution** based on purchasing trends.
## Key Findings & Conclusion
- **Men, on average, spend more than women**, but product categories influence this trend.
- **Customers aged 26-35 make up the largest purchasing group**, driving nearly 40% of total sales.
- **City B customers contribute the most to total revenue**, showing strong demand in Tier-2 locations.
- **Unmarried customers tend to spend more than married customers**.
- **Product categories 1, 5, 8, and 11 are the most purchased items**, indicating strong consumer interest.
## Technologies Used
- **Python** (Pandas, NumPy, Matplotlib, Seaborn)
- **Jupyter Notebook** for data exploration and visualization
- **Statistical Analysis** for hypothesis testing
- **Data Preprocessing & Cleaning**
## How to Use This Repository
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/black-friday-analysis.git
2. **Navigate to the project directory:**
```bash
cd black-friday-analysis
3. **Download & extract the dataset:**
```bash
Unzip "Black Friday Purchase Behavior Analysis.zip"
4. **Open the Jupyter Notebook:**
```bash
jupyter notebook "Black Friday Purchase Behavior Analysis.ipynb"
5. **Run the analysis:**
- Execute the notebook cells sequentially to process and analyze the dataset.
- Review the outputs and visualizations for insights.
6. **Preview the analysis (without running cells):**
```bash
Open "Black Friday Purchase Behavior Analysis (without any cells running).ipynb" to view the notebook contents without execution.
## **Contact & Contributions**
Feel free to explore and contribute! If you have any suggestions, reach out or submit a pull request.
- **Email**: [jordan.c.l.wright@gmail.com](mailto:jordan.c.l.wright@gmail.com)
---
### **Author:** Jordan
[GitHub Profile](https://github.com/JordanConallLuthaisWright)
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